Development of an optimised event reconstruction for the Deep Underground Neutrino Experiment using machine learning and a multi-algorithm approach

Mousam将在Pandora框架[1]中开发模式识别算法，以重建由深地下中微子实验（DUNE）[2]部署的液氩时间投影室（lartpc）中的中微子诱导事件。DUNE旨在帮助回答基础物理学中最重要的突出问题：“我们宇宙中物质-反物质不对称的起源是什么？”它还将提供控制中微子振荡参数的精确测量，并有可能记录来自核心坍缩超新星的中微子爆发，从而提供丰富的新信息。LArTPC模式识别是现代高能物理中最具挑战性的问题之一，是对DUNE的重要贡献。lartpc提供了中微子相互作用中产生的带电粒子的“照片质量”图像。图像可能非常复杂，混合了重叠的轨道状和阵雨状拓扑结构。虽然人类的大脑通常可以挑选出关键特征，但开发一个自动化的算法解决方案是一个重大挑战。模式识别是DUNE工作流程中的一个步骤，其中详细检查LArTPC图像，因此充分提取图像中的信息至关重要。潘多拉项目支持一种“多算法”方法来分析LArTPC图像，其中每个算法都在事件拓扑中寻找特定的特征。数十种算法仔细地构建了事件的图像，并共同提供了健壮的重建。潘多拉目前提供了最先进和记录最好的LArTPC重建，它被国际中微子物理学界广泛使用。穆萨姆将致力于开发新的模式识别算法，以识别DUNE事件的特征。在第一个实例中，Mousam将专注于理解Pandora模式识别DUNE事件的当前性能，识别任何弱点并设计算法来解决特定拓扑的任何问题。穆萨姆将越来越多地关注使用机器学习方法来驱动模式识别算法做出的决策。他将开发算法，利用机器学习将DUNE事件中的单个命中分类为源自轨迹状或阵雨状粒子。他将努力确定DUNE事件中中微子相互作用顶点的位置，并进行第一次研究，以确定次级下游相互作用的顶点。他将确保从机器学习方法中提取的信息被有效地利用，以驱动更高效的模式识别。Mousam最终将开发一个物理分析，利用模式识别输出的详细知识来优化事件的选择，并评估DUNE对控制中微子振荡和/或中微子扇区CP破坏的参数的灵敏度欧元。理论物理。[j] .物理学报（2018）78:82[2]arXiv:1512.06148 [j] .ins-det]

Mousam will develop pattern-recognition algorithms in the Pandora framework[1], to reconstruct neutrino-induced events in the liquid-argon time-projection chambers (LArTPCs) to be deployed by the Deep Underground Neutrino Experiment (DUNE)[2]. DUNE is designed to help answer arguably the most important outstanding question of fundamental physics: "What is the origin of the matter-antimatter asymmetry in our Universe?". It will also provide precision measurements of the parameters governing neutrino oscillations and it has the potential to record a burst of neutrinos from a core-collapse supernova, providing a wealth of new information.LArTPC pattern recognition is one of the most challenging problems in modern high energy physics and is a critical contribution to DUNE. LArTPCs provide "photograph quality" images of the charged particles produced in neutrino interactions. The images can be extremely complex, with a mix of overlapping track-like and shower-like topologies. Whilst the human brain can usually pick out the key features, it is a significant challenge to develop an automated, algorithmic solution. The pattern recognition is the single step in the DUNE workflow in which LArTPC images are examined in detail, so it is vital that information in the images is fully extracted.The Pandora project champions a "multi-algorithm" approach to analysing LArTPC images, in which individual algorithms each look for specific features in event topologies. Many tens of algorithms carefully build up a picture of events and collectively provide a robust reconstruction. Pandora currently offers the most advanced and best documented LArTPC reconstruction and it is used extensively by the international neutrino physics community. Mousam will be working to develop novel pattern-recognition algorithms to identify features in events at DUNE.In the first instance, Mousam will focus on understanding the current performance of the Pandora pattern recognition for DUNE events, identifying any weaknesses and designing algorithms to address any issues with specific topologies. Mousam will increasingly focus on the use of machine-learning approaches to drive the decisions made by pattern-recognition algorithms. He will develop algorithms that use machine-learning to classify individual hits in DUNE events as originating from track-like or shower-like particles. He will work to identify the positions of neutrino interaction vertices in DUNE events and perform the first studies to identify the vertices of secondary, downstream interactions. He will ensure the information extracted from machine-learning approaches is exploited effectively to drive a more performant pattern recognition.Mousam will ultimately develop a physics analysis, using detailed knowledge of the pattern-recognition outputs to optimise selection of events and assess the sensitivity of DUNE to the parameters governing neutrino oscillations and/or CP violation in the neutrino sector.[1] Eur. Phys. J. C (2018) 78: 82[2] arXiv:1512.06148 [physics.ins-det]